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Biology subjects

Nagar, V.

Publications and source records attributed to Nagar, V..

2 recordsLinked to original sources

Proviral dynamics and HIV-1C viral diversity in the context of HIV-TB co-infection

BackgroundART effectively suppresses HIV replication and restores CD4+ T cells; however, long-lived HIV latent reservoirs enable viral persistence. Tuberculosis (TB) co-infection further impacts HIV latency and enhances viral replication. Given the high prevalence of latent TB infection (LTBI) in TB-endemic settings, understanding its impact on HIV biology is critical. Our study aims to investigate the influence of TB co-infection on HIV reservoir dynamics, viral diversity, and drug resistance mutations in ART-naive individuals. MethodologySamples from 90 ART-naive HIV-1C individuals, stratified based on IGRA and TB diagnosis, were used in this study. Plasma and PBMCs were isolated for viral RNA and DNA extraction respectively. Total proviral DNA was quantified using gag PCR. Full-length env and pol genes were amplified, purified and sequenced using ONT and Illumina platforms. Pol sequences were subjected to Drug Resistance Mutation (DRM) analysis via Stanford HIVdb with a minimum threshold mutation frequency of [≥]10%. Full length env sequences were used for phylogenetic analysis by aligning with Indian Subtype C reference sequence and phylogenetic tree was generated using ggplot2. ResultProviral load analysis showed no significant differences across HIV+LTBI-, HIV+LTBI+, and HIV+TB+ groups, although a trend toward higher levels was observed in HIV+TB+ individuals. Correlation analysis revealed distinct immune associations, with HIV+LTBI+ individuals showing positive correlations with activation and PD-1 expression. Longitudinal analysis of proviral loads demonstrated a modest decline in proviral load post-ART but remained persistent for up to 18-20 months following initiation of ART accompanied by low level ongoing viral replication. DRM analysis revealed a 33% prevalence in ART-naive individuals, with higher occurrence in HIV+LTBI+ group. Of the identified DRMs, 38% (5/13) and 71% (5/7) in sequences obtained from PBMC and plasma respectively were attributed to polymorphic mutations associated with Integrase strand transfer inhibitors (INSTIs). DRMs within plasma and PBMC derived viruses showed high concordance. Phylogenetic analysis of env sequences indicated overlapping viral populations between the 3 groups, with greater diversity in PBMCs compared to plasma. ConclusionThe study highlights that HIV reservoir dynamics, drug resistance, and viral diversity are significantly influenced by TB co-infection. While proviral loads were comparable, LTBI-associated immune activation and granuloma niches may have driven viral diversification and DRM emergence. High concordance between compartments and presence of transmitted resistance underscore the need for baseline screening, multi-compartment analysis, and sustained surveillance.

microbiology↗

Improved HIV-1 drug resistance mutation prediction using quasispecies reconstruction supported analysis

Accurate and sensitive approaches to detect HIV-1 drug resistance mutations (DRMs) are indispensable for the paradigm of treatment as prevention. While HIV-1 proviral DNA allows sensitive high throughput sequencing (HTS)-based DRM detection, its applicability is limited by presence of defective genomes. This study demonstrates application of quasispecies reconstruction algorithms (QRAs) to improve DRM detection sensitivity from proviral DNA. A robust benchmarking of 5 QRAs was performed with 2 distinct experimental control-datasets including a stringent, novel control: DCPM, simulating in-vivo variant distribution (0.08%-86.5%). Selected QRA was further evaluated for its ability to differentiate DRMs from hypermutated sequences using an in-silico control. PredictHaplo outperformed all others in terms of precision and was selected for further analysis. Near full-genome HTS was performed on proviral DNA from 20 HIV-1C infected individuals, at different stages of ART, from Mumbai, India. DRM detection was performed through residue-wise variation analysis and implementation of QRAs. Both analyses were highly concordant for DRM frequencies >10% (spearman r=0.91, p<0.0001). Phylogenetic association in HTS datasets with shared transmission history could also be demonstrated by PredictHaplo. This study highlights utility of QRAs as an adjunct to traditional residue-wise variation-based DRM detection leading to optimal personalized ART as well as better disease management.

bioinformatics↗